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Methods for Detecting Short-Term Herding in Financial Markets

Article Quant Q&A · Author: user2921
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Summary

The discussion considers whether herding can be measured over short horizons, including intraday periods. It summarizes two studies: one examines dispersion in US equity returns around large market moves, while another uses disclosed intraday commodity trading by large participants to assess whether their actions align. The first approach is described as a weak proxy because return dispersion is asset-specific and may miss coordinated behavior within groups of similar assets. The response suggests Hawkes process models as a way to represent event clustering, distinguishing events that follow earlier activity from those arising independently. It connects this approach to market impact and order splitting, where metaorder data may reveal coordinated trading. The cited methods are presented as possible starting points, not a settled general test. Short-horizon herding analysis is constrained by limited trading and portfolio composition data, and results may depend heavily on the market and available observations.

Key ideas

  • Return dispersion around large moves can serve as a limited proxy for herding.
  • Intraday disclosures can be used to study whether large participants trade in similar directions.
  • Hawkes models represent events as potentially triggered by earlier activity or arising independently.
  • Metaorder data can help connect herding analysis with market impact and order splitting.
  • Short-horizon findings may be difficult to generalize because data and behavior vary by market.

Tags

Market microstructureStatisticsEquitiesCommodities
Full text
# Testing for stock market herding over short periods


# Testing for stock market herding over short periods












The literature has well established methods for testing stock market herding over a decent time window.

Are there any ways that have appeared in the literature to test for stock market herding over a short time span (e.g. 5 days)?

Perhaps something a bit more than just applying the identical time series models to intraday data (I don't necessarily see anything wrong with this, but would like to know if there's dedicated research on this topic).

## Answer by Student (score 1)

https://quant.stackexchange.com/a/20640

As John mentioned, there are limits to short-term trading or portfolio composition data. However, there are a couple of relevant studies which feature intraday data that I transcribed from this pretty old IMF white paper:

Christie and Huang (1995) studied returns of US stock equities, finding that (controlling for clustering of correlated assets) dispersion on daily and monthly returns is higher at times of large stock movements. However, it's a pretty weak measure of "herding" because it's asset-specific, and overlooks assets of the same individual class/geographical region.

Kodres and Pritsker (1996) analysed public disclosures of intraday commodities trading data from the CFTC (i.e. only large participants), ran correlations/probit to see how likely participants will make the same trades when others are doing the same.

These studies seem to be very vanilla reg methods. I feel that the amount of "herding" is so market-specific and limited by paucity of data that I wouldn't dare generalise; I strongly suspect, however, that traders using technical analysis are implicitly trying to predict herding behaviors.

## Answer by lehalle (score 1)

https://quant.stackexchange.com/a/22574

I guess the best way to test herding using intraday data is to use Hawkes modelling. Hawkes processes capture the fact an event is a consequence of a previous one (endogenous) or totally new (exogenous). A good start is Chapter V of Thibault Jaisson's PhD thesis: Market activity and price impact throughout time scales.

It is of course related to market impact. I would advice to read Market Impacts and the Life Cycle of Investors Orders by Bacry, Iuga, Lasnier and L (a preprint is available here). When you study market impact you often have a database with split metaorders, hence you know part of the herding.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.